An intelligent supervision method and system for the whole process of warehousing work order tasks

Abstract

The present invention relates to the technical field of warehousing supervision, and specifically discloses a full-process intelligent supervision method and system for warehousing work order tasks, including the following steps: obtaining the work order tasks and task information within the work cycle of the warehousing work order tasks, and calculating the execution stability rate of the work order tasks; obtaining a periodic execution stability signal or a periodic optimization review signal; calculating the path deviation rate and comparing it with the path deviation rate threshold; obtaining a work order task path normal signal or a work order task path abnormal signal; calculating the task processing time deviation rate and comparing it with the task processing time deviation rate threshold to obtain a work order task execution abnormal signal or a work order task dual supervision signal; then calculating the execution deviation performance value of the work order task and comparing it with the execution deviation performance qualified value; obtaining a generated execution verification qualified signal or a work order task execution abnormal signal, improving the accuracy and timeliness of warehousing work, and effectively supervising the executors or execution devices during the execution process of warehousing work order tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing supervision, and particularly relates to a full-process intelligent supervision method and system for warehousing work order tasks. Background Art

[0002] Warehousing is an important part of modern logistics, playing a crucial role in the logistics system and being the focus of research and planning by manufacturers. Efficient and reasonable warehousing can help manufacturers speed up the flow of materials, reduce costs, ensure the smooth progress of production, and can achieve effective control and management of resources. The development of warehousing has gone through different historical periods and stages. From primitive manual warehousing to modern intelligent warehousing, with the support of various high-tech technologies for warehousing, the efficiency of warehousing has been greatly improved.

[0003] In the process of warehousing management, the efficiency in the execution of warehousing work orders is extremely important. High-efficiency work order task execution indirectly creates profits for the enterprise. Therefore, supervision in the execution process of warehousing work order tasks is also an important link. However, the existing supervision is all through manual supervision or video monitoring, and it cannot timely and accurately discover the problems in the execution process of warehousing work orders, and cannot provide strong and effective basis for the supervision of warehousing work order tasks. Summary of the Invention

[0004] The purpose of the present invention is to provide a full-process intelligent supervision method and system for warehousing work order tasks to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A full-process intelligent supervision method for warehousing work order tasks includes the following steps:

[0007] Obtain the work order tasks within the work cycle T of the warehousing work order tasks and mark them as i, where i is 1, 2, 3...; obtain the task information in the work order tasks and mark them as n, where n is 1, 2, 3...; obtain the task basic data of each task information in the work order tasks and the execution information data of the work order task i; calculate the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks.

[0008] Compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks with the execution stability rate threshold ZTy; obtain a cycle execution stability signal or a cycle optimization review signal.

[0009] Based on the cycle optimization review signal, obtain the task basic data and execution information data under the work order task completion to-be-optimized signal, and calculate to obtain the path deviation rate LP i; and compare it with the path deviation rate threshold LPy; obtain the work order task path normal signal or the work order task path abnormal signal;

[0010] Based on the work order task path normal signal, obtain the task information under the work order task path normal signal; calculate the task processing time deviation rate TP i ; and compare it with the task processing time deviation rate threshold TPy to obtain the work order task execution abnormal signal or the work order task double-monitoring signal;

[0011] Based on the work order task double-monitoring signal, obtain the path deviation rate LP of the work order task under the work order task double-monitoring signal i and the task processing time deviation rate TP i ;

[0012] Through SZXP i =β×LP i +γ×TP i Calculate the execution deviation performance value SZXP of the work order task i ; where, β is the path planning deviation performance coefficient, γ is the task execution deviation performance coefficient; where, β + γ = 1, and both β and γ are greater than 0;

[0013] Then, compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy; obtain the generated execution verification qualified signal or the work order task execution abnormal signal.

[0014] As a further solution of the present invention: The task basic data includes the task unit completion time t (i，n) , the task target coordinate position D(x, y) (i，n) and the task processing working time value GT (i，n) ;

[0015] The execution information data includes the total work order execution time ZS i , the work order execution travel time value ZX i and the total work order execution travel LS i ;

[0016] The calculation method of the execution stability rate ZT of the work order task is:

[0017] Traverse and calculate the work hour execution efficiency ratio SQB of the work order task within the work order task working cycle T i ;

[0018] Compare the work hour execution efficiency ratio SQB of the work order task within the work order task working cycle T i with the work hour execution efficiency ratio threshold SQY in turn;

[0019] If the working hour execution efficiency ratio SQB i is less than or equal to the working hour execution efficiency ratio threshold SQY, a normal work order task completion signal is obtained;

[0020] If the working hour execution efficiency ratio SQB i is greater than the working hour execution efficiency ratio threshold SQY, a work order task completion signal to be optimized is obtained;

[0021] Obtain the number of normal work order task completion signals within the work cycle T of the warehousing work order task, and denote it as kon;

[0022] Through calculate to obtain the execution stability rate ZT of the work order task within the work cycle T of the warehousing work order task.

[0023] As a further solution of the present invention: the working hour execution efficiency ratio SQB of the work order task i ; the calculation method is:

[0024] Through MZ i =∑t (i，n) calculate to obtain the total target time consumption MZ of the task of work order task i i ; that is, calculate the maximum time value consumed when each piece of execution information in the work order task is completed separately;

[0025] Based on the total target time consumption MZ i and the total work order execution time ZS i , through calculate to obtain the working hour execution efficiency ratio SQB of work order task i i .

[0026] As a further solution of the present invention: compare the execution stability rate ZTon of the work order task within the work cycle T of the warehousing work order task with the execution stability rate threshold ZTy;

[0027] If the execution stability rate ZTon is greater than or equal to the execution stability rate threshold ZTy; generate a periodic execution stability signal;

[0028] If the execution stability rate ZTon is less than the execution stability rate threshold ZTy, generate a periodic optimization review signal.

[0029] As a further solution of the present invention: the calculation method of the path deviation rate LP i is:

[0030] Obtain the task basic data including the task target coordinate position D(x, y) (i，n) ; at the same time, obtain the execution information data including the total work order execution travel LS i ;

[0031] Obtain the starting coordinate point S(x, y) of task i of the work order task (i，0) ; Traverse and calculate to obtain the optimal total travel value of the work order task under the signal to be optimized and mark it as LY i ;

[0032] Among them, the optimal total travel value LY i Use the exhaustive algorithm to calculate the starting coordinate point S(x, y) of task i of the work order task (i，0) Pass through all task target coordinate positions D(x, y) (i，n) The total path value set {LZJ i}, at this time, LY i = minLZJ i ;

[0033] Pass Calculate to obtain the path deviation rate LP of the work order task under the signal to be optimized of the work order task i .

[0034] As a further solution of the present invention: Compare the path deviation rate LP of the work order task under the signal to be optimized of the work order task i with the path deviation rate threshold LPy;

[0035] If the path deviation rate LP i is less than or equal to the path deviation rate threshold LPy, the signal to be optimized for the completion of the work order task is updated to become the normal signal of the work order task path;

[0036] If the path deviation rate LP i is greater than the path deviation rate threshold LPy, keep the signal to be optimized for the completion of the work order task updated to become the abnormal signal of the work order task path.

[0037] As a further solution of the present invention: The task processing time deviation rate TP i ; The calculation method is:

[0038] Obtain the task information of task i of the work order task under the abnormal signal of the work order task path; including the task processing working time value GT in the task (i，n) ; Through RZT i = ∑GT (i，n) Calculate to obtain the total task processing time value RZT of task i of the work order task i ;

[0039] Based on the total task processing time value RZT of task i of the work order task i ; Obtain the total work order execution time ZS and the work order execution travel time value ZX in the execution information data i and the work order execution travel time value ZX i ;

[0040] Pass Calculate the task processing time deviation rate TP i 。

[0041] As a further solution of the present invention: Compare the task processing time deviation rate TP i with the task processing time deviation rate threshold TPy;

[0042] If the task processing time deviation rate TP i is greater than the task processing time deviation rate threshold TPy; Update the normal signal of the work order task path to the abnormal signal of the work order task execution;

[0043] If the task processing time deviation rate TP i is less than or equal to the task processing time deviation rate threshold TPy; Update the normal signal of the work order task path to the double - supervision signal of the work order task.

[0044] As a further solution of the present invention: Compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy;

[0045] If the execution deviation performance value SZXP i is less than or equal to the execution deviation performance qualified value SXZPy, generate an execution verification qualified signal;

[0046] If the execution deviation performance value SZXP i is greater than the execution deviation performance qualified value SXZPy, generate an abnormal signal for the work order task execution.

[0047] As a further solution of the present invention: A full - process intelligent supervision system for warehousing work order tasks, including:

[0048] Data acquisition module: Used to acquire the work order tasks within the work cycle T of the warehousing work order tasks, and mark them as i, where i is 1, 2, 3...; Acquire the task information in the work order tasks, and mark them as n, where n is 1, 2, 3...; Acquire the task basic data of each task information in the work order tasks and the execution information data of the work order task i; Calculate the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks;

[0049] Supervision determination module: Used to compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks with the execution stability rate threshold ZTy; Obtain the periodic execution stability signal or the periodic optimization review signal;

[0050] Single - item verification module: Based on the periodic optimization review signal, acquire the task basic data and execution information data under the work order task completion to - be - optimized signal, and calculate the path deviation rate LP i; and compare it with the path deviation rate threshold LPy; obtain a work order task path normal signal or a work order task path abnormal signal;

[0051] Based on the work order task path normal signal, obtain the task information under the work order task path normal signal; calculate and obtain the task processing time deviation rate TP i ; and compare it with the task processing time deviation rate threshold TPy to obtain a work order task execution abnormal signal or a work order task dual supervision signal;

[0052] Dual verification module: Based on the work order task dual supervision signal, obtain the path deviation rate LP of the work order task under the work order task dual supervision signal i and the task processing time deviation rate TP i ;

[0053] Through SZXP i =β×LP i +γ×TP i Calculate and obtain the execution deviation performance value SZXP of the work order task i ; where β is the path planning deviation performance coefficient, and γ is the task execution deviation performance coefficient; where β + γ = 1, and both β and γ are greater than 0;

[0054] Then, compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy; obtain a generated execution verification qualified signal or a work order task execution abnormal signal.

[0055] Advantages of the present invention:

[0056] In the present invention, by calculating the execution information and task information of the work order task within the work cycle T of the warehousing work order task, it is judged whether the warehousing work order task within the work cycle is abnormal. If there is an unstable execution situation during the execution of the warehousing work order task, it indicates that the execution of some work order tasks is abnormal. At this time, by calculating the path and execution time of the work order task execution, it is further judged whether there is an abnormality during the execution of the work order task, and supervision and reminder are carried out based on the abnormal situation to ensure that the execution efficiency of the work order task meets the requirements, improve the accuracy and timeliness of the warehousing work, effectively supervise the executor or execution equipment during the execution process of the warehousing work order task, and provide an adjustment basis;

[0057] At the same time, if the calculation of the direct execution path deviation rate, task processing time deviation rate, and execution deviation performance value is directly performed, the single task work order can be directly supervised, and the problems occurring during the execution of the task work order can be quickly discovered. Brief Description of the Drawings

[0058] The present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 is a schematic diagram of the method flow of the present invention;

[0060] Figure 2 is a system block diagram of the present invention. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 as shown, the present invention is a full-process intelligent supervision method for warehousing work order tasks, including the following steps:

[0064] Step 01: Obtain the work order tasks within the work cycle T of the warehousing work order task, and mark the work order tasks as i, where i is 1, 2, 3...;

[0065] Obtain the task information in the work order task, and mark the task information as n in sequence, where n is 1, 2, 3...; obtain the task basic data of each task information in the work order task, where the task basic data includes the task unit completion time t (i，n) , the task target coordinate position D(x, y) (i，n) and the task processing working time value GT (i，n) ;

[0066] It should be noted that: the task unit completion time t (i，n) refers to the time value for each single execution of the task information in the work order task; the task processing working time value GT (i，n) refers to the time value of the execution work during each single execution of the task information, such as picking, stacking, palletizing, etc., that is, the execution time of the warehousing work except for the path walking time;

[0067] Obtain the execution information data of the work order task i, and the execution information data includes the total work order execution time ZS i , the work order execution travel time value ZX i and the total work order execution travel value LS i ;

[0068] It should be noted that the total work order execution time ZS i refers to the total duration spent when the work order task is executed; the work order execution travel time value ZXi It refers to the total execution duration of all execution information work in the work order task; the total value LS of the work order execution itinerary i It refers to the total length of the path traveled by the executor or execution device after all execution information in the work order task is executed;

[0069] Step 02: Based on the task basic data and execution information data of the work order task i, calculate the execution stability rate ZT of the work order task within the work cycle T of the warehousing work order task; specifically:

[0070] 021: Traverse and calculate the working hour execution efficiency ratio SQB of the work order task within the work cycle T of the warehousing work order task i ; including:

[0071] 0211: Through MZ i =∑t (i，n) Calculate the total target time consumption MZ of the work order task i i ; that is, calculate the maximum time value consumed when each execution information in the work order task is completed separately;

[0072] 0212: Based on the total target time consumption MZ i and the total work order execution time ZS i , through Calculate the working hour execution efficiency ratio SQB of the work order task i i ; By calculating the formula execution efficiency ratio of the work order task, it can be obtained that the ratio of the total work order execution time consumed by each work order task after optimization to the total target time consumption. Among them, the smaller the ratio, the better the optimization of the work order task;

[0073] 022: Compare the working hour execution efficiency ratio SQB of the work order task within the work cycle T of the warehousing work order task i with the working hour execution efficiency ratio threshold SQY in turn;

[0074] If the working hour execution efficiency ratio SQB i is less than or equal to the working hour execution efficiency ratio threshold SQY, a work order task completion normal signal is obtained, indicating that the timeliness of this work order task is better;

[0075] If the working hour execution efficiency ratio SQB i is greater than the working hour execution efficiency ratio threshold SQY, a work order task completion to-be-optimized signal is obtained, indicating that the completion efficiency of this work order task is poor;

[0076] 023: Obtain the number of work order task completion normal signals within the work cycle T of the warehousing work order task, and record it as kon;

[0077] Through Calculate the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks;

[0078] Step 03: Compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks with the execution stability rate threshold ZTy;

[0079] If the execution stability rate ZTon is greater than or equal to the execution stability rate threshold ZTy; generate a periodic execution stability signal; that is, it indicates that within the work cycle T of the warehousing work order tasks, the execution stability rate of the work order tasks is relatively high, and the proportion of the task information in the work order tasks that has been optimized during execution meets the requirements of execution stability;

[0080] If the execution stability rate ZTon is less than the execution stability rate threshold ZTy, generate a periodic optimization review signal; at this time, it indicates that within the work cycle T of the warehousing work order tasks, the execution stability rate of the work order tasks is relatively low, and the execution time of a relatively large number of work order tasks does not meet the optimization standard;

[0081] Therefore, it is necessary to review the execution process of the work order tasks under the to-be-optimized signal for the work order tasks within the work cycle to find out where the time-consuming problem occurs in the execution process of the work order tasks;

[0082] Step 04: Based on the periodic optimization review signal, obtain the basic data and execution information data of the work order tasks under the to-be-optimized signal for the work order tasks; calculate and obtain the path deviation rate LP i ;

[0083] Including the following steps:

[0084] 041: Obtain the task basic data including the task target coordinate position D(x, y) (i，n) ; At the same time, obtain the execution information data including the total value of the work order execution itinerary LS i ;

[0085] 042: Obtain the starting coordinate point S(x, y) of the work order task i (i，0) ; Traverse and calculate the optimal total value of the itinerary of the work order tasks under the to-be-optimized signal and mark it as LY i ;

[0086] The starting coordinate point of the task refers to the position point in the warehousing warehouse where the executor or the execution device is located when each task work order starts to be executed;

[0087] Among them, the optimal total value of the itinerary LY i Use the exhaustive algorithm to calculate the starting coordinate point S(x, y) of the work order task i (i，0) Pass through all the task target coordinate positions D(x, y) (i，n) The total path value set {LZJ i}, at this time, LYi = minLZJ i ;

[0088] The set of total path values of the task starting coordinate point passing through all task target coordinate positions refers to: the set of all total path values walked by the executor or execution device starting from the task starting coordinate point through the task target coordinate positions of all work order tasks to complete the task execution;

[0089] 043: Through Calculate the path deviation rate LP of the work order task under the work order task completion to-be-optimized signal i ;

[0090] Step 05: Compare the path deviation rate LP of the work order task under the work order task completion to-be-optimized signal i with the path deviation rate threshold LPy;

[0091] If the path deviation rate LP i is less than or equal to the path deviation rate threshold LPy, the work order task completion to-be-optimized signal is updated to become the work order task path normal signal;

[0092] If the path deviation rate LP i is greater than the path deviation rate threshold LPy, keep the work order task completion to-be-optimized signal updated to become the work order task path abnormal signal;

[0093] Perform path calculation on the work order task under the periodic optimization review signal, and calculate the path deviation rate of the work order task; then compare the path deviation rate of the work order task with the path deviation rate threshold to further judge whether there is a deviation in path optimization when the working hour execution efficiency ratio of the work order task does not reach the standard; at the same time, it can be judged that when there is less execution information in the work order task, whether the working hour execution efficiency ratio does not meet the requirements is due to path planning and execution reasons; for example, if there is only 1 execution information in the work order task and there is only 1 path at this time, it is possible that the working hour execution efficiency ratio is greater than the working hour execution efficiency ratio threshold. Therefore, calculating the path deviation rate of the work order task can judge whether there is an abnormality in the path planning of the work order task. If the work order task path is abnormal, remind the executor to execute according to the planned path when walking the path, or remind the background operator of the execution device to analyze the execution path of the work order task to judge whether there is waiting or other interference during the progress;

[0094] Step 06: Based on the work order task path normal signal, obtain the task information under the work order task path normal signal; calculate the task processing time deviation rate TP i ;

[0095] Including:

[0096] 061: Obtain the task information of work order task i under the abnormal signal of the work order task path; including the task processing working time value GT in the task (i，n) ; Through RZT i = ∑GT (i，n) Calculate the total task processing time value RZT of work order task i i ;

[0097] 062: Based on the total task processing time value RZT of work order task i i ; Obtain the total work order execution time value ZS and the work order execution travel time value ZX in the execution information data i and the work order execution travel time value ZX i ;

[0098] Through Calculate the task processing time deviation rate TP i ;

[0099] When the path deviation rate in the work order task is in a qualified state, to further determine the reason for the work order task completion pending optimization signal during the work process of the work order task, at this time, further analyze and calculate the execution task processing time in the work order task, and obtain whether there is an abnormality during the execution of the work order task under the normal signal state of the work order task path, such as instability of the executor or execution device during work;

[0100] Step 07: Compare the task processing time deviation rate TP of the work order task under the normal signal of the work order task path i with the task processing time deviation rate threshold TPy;

[0101] If the task processing time deviation rate TP i is greater than the task processing time deviation rate threshold TPy; the normal signal of the work order task path is updated to the abnormal signal of the work order task execution; at this time, it indicates that during the execution of the work order task, there is an abnormal situation, such as the executor being lazy, absent from work, or the execution device having a power failure or long waiting problems, then the execution process can be sub - supervised;

[0102] If the task processing time deviation rate TP i is less than or equal to the task processing time deviation rate threshold TPy; the normal signal of the work order task path is updated to the dual - supervision signal of the work order task; at this time, it indicates that both the path deviation rate and the task processing time deviation rate are within the normal range during the task execution process, and due to the relatively high working efficiency of this work order task, the execution path and the task execution time should be considered simultaneously to determine whether the execution process of this work order task is normal;

[0103] Step 08: Based on the dual-monitoring signal of the work order task, obtain the path deviation rate LP of the work order task under the dual-monitoring signal of the work order task i and the task processing time deviation rate TP i ;

[0104] Through SZXP i = β × LP i + γ × TP i Calculate to obtain the execution deviation performance value SZXP of the work order task i ; where, β is the path planning deviation performance coefficient, and γ is the task execution deviation performance coefficient; where, β + γ = 1, and both β and γ are greater than 0;

[0105] Then, compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy;

[0106] If the execution deviation performance value SZXP i is less than or equal to the execution deviation performance qualified value SXZPy, generate an execution verification qualified signal; indicating that the task execution of this work order is normal. At this time, this work order task can be recorded as a work order task completion normal signal; then execute 023;

[0107] If the execution deviation performance value SZXP i is greater than the execution deviation performance qualified value SXZPy, generate a work order task execution abnormal signal; at this time, then review the execution path and execution process during the execution of the work order task to determine whether there is an abnormal situation during the execution process.

[0108] By calculating the execution information and task information of the work order task within the work cycle T of the warehousing work order task, determine whether there is an abnormality in the warehousing work order task within the work cycle. If there is an unstable execution situation during the execution of the warehousing work order task, it indicates that the execution of some work order tasks has an abnormality. At this time, then calculate the execution path and execution time of the work order task to further determine whether there is an abnormality during the execution of the work order task, and conduct supervision and reminder based on the abnormal situation to ensure that the execution efficiency of the work order task meets the requirements, improve the accuracy and timeliness of warehousing work, effectively supervise the executor or execution equipment during the execution process of the warehousing work order task, and provide an adjustment basis;

[0109] Meanwhile, during the working process of the above technical solution, if the calculation of the direct path deviation rate, task processing time deviation rate, and execution deviation performance value is directly performed, the supervision of a single task work order can be directly carried out, and the problems occurring during the execution process of the task work order can be quickly discovered.

[0110] Embodiment 2

[0111] Refer toFigure 2 As shown in the figure, an intelligent supervision system for the whole process of warehousing work order tasks includes:

[0112] Data acquisition module: used to acquire work order tasks within the work cycle T of warehousing work order tasks, and mark them as i, where i is 1, 2, 3...; acquire task information in the work order tasks, and mark them as n, where n is 1, 2, 3...; acquire the task basic data of each task information in the work order tasks and the execution information data of work order task i; calculate the execution stability rate ZTon of work order tasks within the work cycle T of warehousing work order tasks.

[0113] Supervision judgment module: used to compare the execution stability rate ZTon of work order tasks within the work cycle T of warehousing work order tasks with the execution stability rate threshold ZTy; obtain a cycle execution stability signal or a cycle optimization review signal.

[0114] Single verification module: Based on the cycle optimization review signal, acquire the task basic data and execution information data of work order tasks under the work order task completion to-be-optimized signal, and calculate to obtain the path deviation rate LP i ; and compare it with the path deviation rate threshold LPy; obtain a work order task path normal signal or a work order task path abnormal signal.

[0115] Based on the work order task path normal signal, acquire the task information under the work order task path normal signal; calculate to obtain the task processing time deviation rate TP i ; and compare it with the task processing time deviation rate threshold TPy to obtain a work order task execution abnormal signal or a work order task double supervision signal.

[0116] Double verification module: Based on the work order task double supervision signal, acquire the path deviation rate LP of work order tasks under the work order task double supervision signal i and the task processing time deviation rate TP i ;

[0117] Through SZXP i =β×LP i +γ×TP i Calculate to obtain the execution deviation performance value SZXP of work order tasks i ; where β is the path planning deviation performance coefficient, and γ is the task execution deviation performance coefficient; where β + γ = 1, and both β and γ are greater than 0;

[0118] Then, compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy; obtain a generated execution verification qualified signal or a work order task execution abnormal signal.

[0119] By calculating the path and execution time of the work order task, further determine whether an abnormality occurs during the execution of the work order task, and based on the abnormal situation, conduct supervision reminders to ensure that the execution efficiency of the work order task meets the requirements, improve the accuracy and timeliness of warehousing work, effectively supervise the executor or execution equipment during the execution process of the warehousing work order task, and provide a basis for adjustment.

[0120] The above has described an embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An intelligent supervision method for the whole process of warehousing work order tasks, characterized in that, Including the following steps: Obtain the work order tasks within the work cycle T of the warehousing work order task, and label them as i, where i is 1, 2, 3...; obtain the task information in the work order task, and label it as n, where n is 1, 2, 3...; obtain the task basic data of each task information in the work order task and the execution information data of the work order task i; calculate the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order task. The task-based data includes the task unit completion time t （i，n） , the task target coordinate position D(x, y) （i，n） and the task processing working time value GT （i，n） ; The execution information data includes the total work order execution time ZS i , the work order execution travel time value ZX i and the total work order execution travel distance LS i ; The calculation method of the execution stability rate ZTon of the work order task is as follows: Traverse and calculate the man-hour execution efficiency ratio SQB of the work order tasks within the work cycle T of the warehousing work order tasks i ; Compare the man-hour execution efficiency ratio SQB of the work order tasks within the work cycle T of the warehousing work order tasks i with the man-hour execution efficiency ratio threshold SQY in sequence; If the working hour execution efficiency ratio is less than or equal to SQB i less than or equal to the working hour execution efficiency ratio threshold SQY, a normal work order task completion signal is obtained; If the labor-hour execution efficiency ratio is greater than the labor-hour execution efficiency ratio threshold SQY, a work order task completion pending optimization signal is obtained; i If the labor-hour execution efficiency ratio is greater than the labor-hour execution efficiency ratio threshold SQY, a work order task completion pending optimization signal is obtained; Obtain the number of normal completion signals of the work order tasks within the work cycle T of the warehousing work order task, and record it as kon. By calculating, the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks is obtained; Compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order task with the execution stability rate threshold ZTy; obtain a periodic execution stability signal or a periodic optimization review signal. Based on the cycle optimization review signal, obtain the basic data and execution information data of the work order task under the work order task completion signal to be optimized, and calculate the path deviation rate LP i ; and compare it with the path deviation rate threshold LPy; obtain a normal signal of the work order task path or an abnormal signal of the work order task path; Based on the normal signal of the work order task path, obtain the task information under the normal signal of the work order task path; calculate and obtain the task processing time deviation rate TP i ; and compare it with the task processing time deviation rate threshold TPy to obtain the abnormal signal of the work order task execution or the dual monitoring signal of the work order task; Based on the dual-monitoring signal of the work order task, obtain the path deviation rate LP of the work order task under the dual-monitoring signal of the work order task i and the task processing time deviation rate TP i ; By calculating, the execution deviation performance value SZXP of the work order task is obtained i ; where β is the path planning deviation performance coefficient, and γ is the task execution deviation performance coefficient; where β + γ = 1, and both β and γ are greater than 0; Then, the execution deviation performance value SZXP i is compared with the execution deviation performance qualified value SXZPy; an execution verification qualified signal or a work order task execution exception signal is generated.

2. The intelligent supervision method for the whole process of a warehousing work order task according to claim 1, wherein The man-hour execution efficiency of the work order task is higher than that of SQB i ; The calculation method is as follows: By calculate the total target time consumption MZ of work order task i i ; that is, calculate the maximum time value consumed when the execution information in the work order task is completed separately for each one; Total time consumption value MZ based on task objectives i and total work order execution time value ZS i , through calculate the work-hour execution efficiency ratio SQB of work order task i i .

3. The intelligent supervision method for the whole process of a warehousing work order task according to claim 1, wherein, Compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order task with the execution stability rate threshold ZTy. If the execution stability rate ZTon is greater than or equal to the execution stability rate threshold ZTy, generate a periodic execution stability signal. If the execution stability rate ZTon is less than the execution stability rate threshold ZTy, generate a periodic optimization review signal.

4. A full-process intelligent supervision method for warehousing work order tasks according to claim 1, characterized in that, The path deviation rate LP i is calculated as follows: Obtaining task basic data includes the task target coordinate position D(x, y) （i，n） ; At the same time, obtaining execution information data includes the total value LS of the work order execution itinerary i ; Obtain the starting coordinate point S(x, y) of work order task i （i，0） ; Traverse and calculate to obtain the optimal total travel value of the work order task under the signal to be optimized and mark it as LY i ; Among them, the optimal total travel value LY i The starting coordinate point S(x, y) of the task of work order task i is obtained by using the exhaustive algorithm （i，0） Passing through all task target coordinate positions D(x, y) （i，n） The total path value set {LZJ i}, at this time, ; By calculating, the path deviation rate LP of the work order task under the signal that the work order task to be optimized for completion is obtained i .

5. A full-process intelligent supervision method for warehousing work order tasks according to claim 4, characterized in that Compare the path deviation rate LP of the work order task under the work order task completion pending optimization signal i with the path deviation rate threshold LPy; If the path deviation rate LP i is less than or equal to the path deviation rate threshold LPy, the work order task completion pending optimization signal is updated to the work order task path normal signal; If the path deviation rate LP i is greater than the path deviation rate threshold LPy, keep the work order task completion pending optimization signal updated to become the work order task path anomaly signal.

6. The intelligent supervision method for the whole process of a warehousing work order task according to claim 1, wherein, The task processing time deviation rate TP i ; is calculated as follows: Obtain the task information of work order task i under the abnormal signal of the work order task path; including the task processing working time value GT in the task （i，n） ; Through Calculate to obtain the total task processing time value RZT of work order task i i ; Total task processing time value RZT based on work order task i i ; Obtain the total work order execution time ZS in the execution information data i and the work order execution travel time value ZX i ; By calculating, the task processing time deviation rate TP is obtained i .

7. A full-process intelligent supervision method for warehousing work order tasks according to claim 6, characterized in that Compare the task processing time deviation rate TP i with the task processing time deviation rate threshold TPy; If the task processing time deviation rate TP i is greater than the task processing time deviation rate threshold TPy, the normal signal of the work order task path is updated to an abnormal signal for the execution of the work order task; If the task processing time deviation rate TP i is less than or equal to the task processing time deviation rate threshold TPy, the normal signal of the work order task path is updated to the dual-monitoring signal of the work order task.

8. The intelligent supervision method for the whole process of a warehousing work order task according to claim 1, characterized in that, Compare the execution deviation performance value SZXP i with the execution deviation performance qualified value SXZPy; If the execution deviation performance value SZXP i is less than or equal to the execution deviation performance qualified value SXZPy, a qualified execution verification signal is generated; If the execution deviation performance value SZXP i is greater than the execution deviation performance qualified value SXZPy, generate a work order task execution exception signal.

9. An intelligent supervision system for the whole process of warehousing work order tasks, characterized in that, This system is used to execute the supervision method described in any one of the above claims 1-8, including: Data acquisition module: used to obtain the work order tasks within the work cycle T of the warehousing work order task, and label them as i, where i is 1, 2, 3...; obtain the task information in the work order task, and label it as n, where n is 1, 2, 3...; obtain the task basic data of each task information in the work order task and the execution information data of the work order task i; calculate the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order task. The task-based data includes the task unit completion time t （i，n） , the task target coordinate position D(x, y) （i，n） and the task processing working time value GT （i，n） ; The execution information data includes the total work order execution time value ZS i , the work order execution travel time value ZX i and the total work order execution travel value LS i ; The calculation method of the execution stability rate ZTon of the work order task is as follows: Traverse and calculate the working hour execution efficiency ratio SQB of the work order tasks within the working cycle T of the warehousing work order tasks i ; Compare the labor-hour execution efficiency ratio SQB of the work order tasks within the work cycle T of the warehousing work order tasks i with the labor-hour execution efficiency ratio threshold SQY in sequence; If the working hour execution efficiency ratio is less than or equal to SQB i less than or equal to the working hour execution efficiency ratio threshold SQY, a normal signal for work order task completion is obtained; If the working hour execution efficiency ratio SQB i is greater than the working hour execution efficiency ratio threshold SQY, a work order task completion pending optimization signal is obtained; Obtain the number of normal completion signals of the work order tasks within the work cycle T of the warehousing work order task, and record it as kon. By calculating, the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order tasks is obtained; Supervision determination module: used to compare the execution stability rate ZTon of the work order tasks within the work cycle T of the warehousing work order task with the execution stability rate threshold ZTy; obtain a periodic execution stability signal or a periodic optimization review signal. Single verification module: Based on the periodically optimized review signal, obtain the basic data and execution information data of the work order task under the work order task completion pending optimization signal, and calculate the path deviation rate LP i ; and compare it with the path deviation rate threshold LPy; obtain the work order task path normal signal or the work order task path abnormal signal; Based on the normal signal of the work order task path, obtain the task information under the normal signal of the work order task path; calculate and obtain the task processing time deviation rate TP i ; and compare it with the task processing time deviation rate threshold TPy to obtain the abnormal signal of the work order task execution or the dual-monitoring signal of the work order task; Dual verification module: Based on the dual monitoring signals of the work order task, obtain the path deviation rate LP of the work order task under the dual monitoring signals of the work order task i and the task processing time deviation rate TP i ; By calculating, the execution deviation performance value SZXP of the work order task is obtained i ; where β is the path planning deviation performance coefficient, and γ is the task execution deviation performance coefficient; where β + γ = 1, and both β and γ are greater than 0; Then, the execution deviation performance value SZXP i is compared with the execution deviation performance qualified value SXZPy; an execution verification qualified signal or a work order task execution abnormal signal is generated.

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